The Mood of Philosophers¶

Name: Linda LIn¶

Introduction¶

Philosophy is the study of fundamental questions about existence, knowledge, values, reason, mind, and language. It explores the nature of reality, the meaning of life, and the essence of truth. It is also mysterious to many people. As a student majoring in math and statistics, the knowledge I know about philosophy is very rare. In this project, I will use the dataset History of Philosophy to explore about philosophy, and trying to figure out the difference of mood among different schools of philosophy

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import nltk
from wordcloud import WordCloud, STOPWORDS
import collections
import string
import seaborn as sns


from nltk.sentiment import SentimentIntensityAnalyzer

Part 1 Exploring Data¶

Firstly, I load the dataframe from Kaggle, and look at these basic information about this dataset.

In [2]:
# read dataframe
df = pd.read_csv("data/philosophy_data.csv")
df.head()
Out[2]:
title author school sentence_spacy sentence_str original_publication_date corpus_edition_date sentence_length sentence_lowered tokenized_txt lemmatized_str
0 Plato - Complete Works Plato plato What's new, Socrates, to make you leave your ... What's new, Socrates, to make you leave your ... -350 1997 125 what's new, socrates, to make you leave your ... ['what', 'new', 'socrates', 'to', 'make', 'you... what be new , Socrates , to make -PRON- lea...
1 Plato - Complete Works Plato plato Surely you are not prosecuting anyone before t... Surely you are not prosecuting anyone before t... -350 1997 69 surely you are not prosecuting anyone before t... ['surely', 'you', 'are', 'not', 'prosecuting',... surely -PRON- be not prosecute anyone before ...
2 Plato - Complete Works Plato plato The Athenians do not call this a prosecution b... The Athenians do not call this a prosecution b... -350 1997 74 the athenians do not call this a prosecution b... ['the', 'athenians', 'do', 'not', 'call', 'thi... the Athenians do not call this a prosecution ...
3 Plato - Complete Works Plato plato What is this you say? What is this you say? -350 1997 21 what is this you say? ['what', 'is', 'this', 'you', 'say'] what be this -PRON- say ?
4 Plato - Complete Works Plato plato Someone must have indicted you, for you are no... Someone must have indicted you, for you are no... -350 1997 101 someone must have indicted you, for you are no... ['someone', 'must', 'have', 'indicted', 'you',... someone must have indict -PRON- , for -PRON- ...
In [3]:
df.describe()
Out[3]:
original_publication_date corpus_edition_date sentence_length
count 360808.000000 360808.000000 360808.000000
mean 1326.800908 1995.155642 150.790964
std 951.492193 23.002287 104.822072
min -350.000000 1887.000000 20.000000
25% 1641.000000 1991.000000 75.000000
50% 1817.000000 2001.000000 127.000000
75% 1949.000000 2007.000000 199.000000
max 1985.000000 2016.000000 2649.000000
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 360808 entries, 0 to 360807
Data columns (total 11 columns):
 #   Column                     Non-Null Count   Dtype 
---  ------                     --------------   ----- 
 0   title                      360808 non-null  object
 1   author                     360808 non-null  object
 2   school                     360808 non-null  object
 3   sentence_spacy             360808 non-null  object
 4   sentence_str               360808 non-null  object
 5   original_publication_date  360808 non-null  int64 
 6   corpus_edition_date        360808 non-null  int64 
 7   sentence_length            360808 non-null  int64 
 8   sentence_lowered           360808 non-null  object
 9   tokenized_txt              360808 non-null  object
 10  lemmatized_str             360808 non-null  object
dtypes: int64(3), object(8)
memory usage: 30.3+ MB

As you can notice, there are are ten columns in this dataset and most of them are different versions of the sentences. Each row represents a text. It also includes the title, school, author, and date of the sentences. Before this projec, I did not know anything about philosophy at all. However, when I saw the column school, I realize that there are several different schools of philosophy. There are 13 different schools and I plot a bar plot to view the number of text in each school. It shows that analytic has the most text whereas stoicism has the least text. Therefore we may guess that analytic has more authors than stoicism. We will use code to prove whether this guess is correct later.

In [5]:
# number of schools
df['school'].nunique()
Out[5]:
13
In [6]:
df['school'].value_counts().plot(kind = 'bar')
Out[6]:
<AxesSubplot: >

In order to prove the guess, I made a bar plot with school on x-axis and number of author on the y-axis. We can see that the result is different from what I thought. Analytic has the most authors, but aristole has only one author who wrote the second most texts, so there is not obvious relationship between number of text and number of authors.

In [7]:
df_author = df.groupby(['school'])['author'].nunique()
df_author.plot(kind='bar', title='number of author of each school', ylabel='number of author', xlabel='school')
Out[7]:
<AxesSubplot: title={'center': 'number of author of each school'}, xlabel='school', ylabel='number of author'>

We also can dig into how the original publiction date is relate to schools. I created a bar plot with schools on the x-axis and the mean number of original publiction date on the y-axis. By doing so, we could have a sense of the active publiction period of each school. Based on the plot, we can see that aristotle, plato, and stoicism are relatively much earlier than the other schools.

In [8]:
df_publication_date = df.groupby(['school'])['original_publication_date'].mean()
df_publication_date.plot(kind='bar', title='publiction date of each school', ylabel='original_publication_date', xlabel='school')
Out[8]:
<AxesSubplot: title={'center': 'publiction date of each school'}, xlabel='school', ylabel='original_publication_date'>

Moreover, I want to find out more characteristic about different schools and the writing style of different schools, so I wonder maybe some schools like longer text and some like shorter text. I plot two bar graph both with schools on x-axis, but one with mean sentence length on y and the other one with mean tokinize number on the y-axis. From the plots we can see that the overall trend for this two plots are very similar. Analystic, nietzsche, and plato has relitively fewer words than others, but there's not much distinctions among different schools.

In [9]:
df_sentence_length = df.groupby(['school'])['sentence_length'].mean()
df_sentence_length.plot(kind='bar', title='mean sentence length of each school', ylabel='mean sentence length', xlabel='school')
Out[9]:
<AxesSubplot: title={'center': 'mean sentence length of each school'}, xlabel='school', ylabel='mean sentence length'>
In [10]:
df['tokenized_number'] = list(df['tokenized_txt'].apply(lambda x: len(x.split())))
df_tokenized_number = df.groupby(['school'])['tokenized_number'].mean()
df_tokenized_number.plot(kind='bar', title='mean tokenizednumber of each school', ylabel='mean tokenized number', xlabel='school')
Out[10]:
<AxesSubplot: title={'center': 'mean tokenizednumber of each school'}, xlabel='school', ylabel='mean tokenized number'>

Part 2 Word Cloud¶

In this part, we want to find out the most common words that philosophers would use. I will use word cloud to visualize the most frequent words.

To begin with the entire dataset, I will perform the word cloud and analysis with the lemmatized_str column which combine words with different forms to one word. I notice this column transform words like 'you' to 'PRON', so other than the regular stopwords, I need to get rid of 'PRON' as well. Also, the most frequent words does not conain punctuations, so I need to remove all punctuations. After the cleaning and analysing, we can easily find out that the most frequent words in the entire dataset are 'one', 'will', 'thing', 'say', 'man', 'may', 'make', 'must', 's', 'time'.

In [11]:
stopwords = set(STOPWORDS)
# let all lemmatized_str to be one string without stopwords
text = " ".join(i for i in df['lemmatized_str'].astype(str))
stopwords.update(['PRON'])
wordcloud = WordCloud(stopwords=stopwords, background_color="white", width=600, height=500).generate(text)
plt.axis("off")
plt.tight_layout(pad=0)
plt.figure(figsize=(8,8))
plt.imshow(wordcloud)
plt.show()
In [12]:
text = " ".join(i for i in df['lemmatized_str'].astype(str))
clean_text = text.translate(str.maketrans('', '', string.punctuation))
clean_words = [word for word in clean_text.split() if word not in stopwords]
counted_words = collections.Counter(clean_words)
# get the top 10 most common words in the sentence
counted_words.most_common(10)
Out[12]:
[('one', 50271),
 ('will', 29977),
 ('thing', 28719),
 ('say', 26087),
 ('man', 24896),
 ('may', 22697),
 ('make', 22110),
 ('must', 21131),
 ('s', 18000),
 ('time', 17247)]

Now, we can perform the same approach on each school to see the difference of common words using by different schools. The results show that the common words for each schools are capitalism: 'great','price','may', 'country', 'upon', 'will', 'produce', 'money', 'part', 'value' nietzsche: 'one', 'man', 'will', 'thou', 'even', 'thing', 'say', 'good', 'ye', 'great' continental: 'one', 'form', 'madness', 's', 'difference', 'make', 'man', 'language', 'time', 'order' aristotle: 'one','thing','man','will', 'must', 'say', 'part', 'animal', 'good', 'case' empiricism: 'idea', 'one', 'may', 'man', 'make', 'mind', 'will', 'thing', 'object', 'without' communism: 'labour', 'value', 'work', 'one', 'capital', 'production', 'commodity', 'form', 'time','power', plato:'say','one', 'thing', 'will', 'man', 'good', 'think', 'make', 'well', 'now' phenomenology: 'world', 'one','thing', 'way', 'time', 'take', 'object', 'experience', 'make', 'present' german_idealism: 'one', 'concept', 'object', 'self', 'reason', 'nature', 'consciousness', 'will', 'determination', 'thing' analytic: 'one', 'may', 'say', 's', 'case', 'true', 'know', 'will', 'give', 'sense' stoicism:'thou', 'thing', 'man', 'unto', 'thy', 'one', 'thee', 'nature', 'will', 'either' rationalism: 'God', 'will', 'thing', 'one', 'body', 'mind', 'man', 'idea', 'say', 'make' feminism:'woman', 'man', 's', 'one', 'will', 'love', 'make', 'life', 'child', 'give'

These schools' most common words are quite different from each other. Due to the reason that their main focus and topics are different, so the words they frequently use will be different.

In [13]:
schools = list(set(df['school']))
for school in schools:
    print(school)
    df_school = df[df['school'] == school]
    text = " ".join(i for i in df_school['lemmatized_str'].astype(str))

    # get the word cloud for each school

    wordcloud = WordCloud(stopwords=stopwords, background_color="white", width=600, height=500).generate(text)
    plt.axis("off")
    plt.tight_layout(pad=0)
    plt.figure(figsize=(8,8))
    plt.imshow(wordcloud)
    plt.show()
    
    text = " ".join(i for i in df_school['lemmatized_str'].astype(str))
    clean_text = text.translate(str.maketrans('', '', string.punctuation))
    clean_words = [word for word in clean_text.split() if word not in stopwords]
    counted_words = collections.Counter(clean_words)
    # printing out the most common words in sentence for each school
    print(counted_words.most_common(10))

feminism

[('woman', 6038), ('man', 3050), ('s', 2825), ('one', 1770), ('will', 1635), ('love', 1180), ('make', 1121), ('life', 1090), ('child', 1018), ('give', 919)]
empiricism

[('idea', 5752), ('one', 3376), ('may', 2996), ('man', 2971), ('make', 2545), ('mind', 2380), ('will', 2189), ('thing', 2041), ('object', 1728), ('without', 1552)]
aristotle

[('one', 9730), ('thing', 7366), ('man', 5642), ('will', 5434), ('must', 4469), ('say', 4410), ('part', 4020), ('animal', 3788), ('good', 3362), ('case', 3293)]
communism

[('labour', 3322), ('value', 2733), ('work', 1950), ('one', 1806), ('capital', 1526), ('production', 1454), ('commodity', 1390), ('form', 1249), ('time', 1201), ('power', 1167)]
plato

[('say', 5842), ('one', 5578), ('thing', 4605), ('will', 4473), ('man', 3403), ('good', 2905), ('think', 2808), ('make', 2762), ('well', 2536), ('now', 2447)]
german_idealism

[('one', 7311), ('concept', 5603), ('object', 3995), ('self', 3750), ('reason', 3664), ('nature', 3344), ('consciousness', 3200), ('will', 3114), ('determination', 3082), ('thing', 3069)]
capitalism

[('great', 2875), ('price', 2629), ('may', 2426), ('country', 2313), ('upon', 2210), ('will', 2130), ('produce', 1905), ('money', 1895), ('part', 1872), ('value', 1849)]
analytic

[('one', 6460), ('may', 5970), ('say', 5732), ('s', 3668), ('case', 3377), ('true', 3057), ('know', 3040), ('will', 2893), ('give', 2761), ('sense', 2741)]
rationalism

[('God', 3508), ('will', 3223), ('thing', 3184), ('one', 3101), ('body', 2952), ('mind', 2564), ('man', 2496), ('idea', 2202), ('say', 2050), ('make', 1845)]
nietzsche

[('one', 2124), ('man', 1504), ('will', 965), ('thou', 848), ('even', 838), ('thing', 782), ('say', 738), ('good', 688), ('ye', 674), ('great', 663)]
continental

[('one', 4219), ('form', 3158), ('madness', 2277), ('s', 1908), ('difference', 1865), ('make', 1818), ('man', 1811), ('language', 1809), ('time', 1755), ('order', 1711)]
phenomenology

[('world', 4349), ('one', 2864), ('thing', 2257), ('way', 2005), ('time', 1878), ('take', 1774), ('object', 1706), ('experience', 1697), ('make', 1673), ('present', 1629)]
stoicism

[('thou', 791), ('thing', 687), ('man', 472), ('unto', 436), ('thy', 338), ('one', 322), ('thee', 306), ('nature', 278), ('will', 258), ('either', 251)]

Part3 Sentiment Analysis¶

Speaking about philosophy, the thing I am most curious about is whether there is a difference of mood between different schools. For example, one school is always pessimistic and the other one is optimistic. Therefore, I am going to use the sentiment intensity analyzer to create a column of ploarity which is the polarity of each text. There is one feature called compound in the polarity. If the compound is positive, then the mood of the text is positive. created a column called sentiment that has the result I got from analysing the compound, which telling me whether the text is positive or not. I tried to graph a bar plot of the number of each sentiment against each school. I found that analytic has the most positive, negative, and neutral text. I think the reason is because analytic has the most amount of text. Therefore, I realized, in order to solve the problem, I need to use the ratio of each sentiment instead.

In [14]:
df['lemmatized_no_punc'] = df['lemmatized_str'].str.replace(r'[^\w\s]+', '')
# create a new column with no stop words and punctuations
df['lemmatized_clean'] = df['lemmatized_no_punc'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stopwords)]))

/var/folders/f0/3c3d31716rzd7nlkfrhvm19h0000gn/T/ipykernel_34387/2280271779.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  df['lemmatized_no_punc'] = df['lemmatized_str'].str.replace(r'[^\w\s]+', '')
In [15]:
analyze = SentimentIntensityAnalyzer()
df['polarity'] = df['lemmatized_clean'].apply(lambda x: analyze.polarity_scores(x))
df.head()
Out[15]:
title author school sentence_spacy sentence_str original_publication_date corpus_edition_date sentence_length sentence_lowered tokenized_txt lemmatized_str tokenized_number lemmatized_no_punc lemmatized_clean polarity
0 Plato - Complete Works Plato plato What's new, Socrates, to make you leave your ... What's new, Socrates, to make you leave your ... -350 1997 125 what's new, socrates, to make you leave your ... ['what', 'new', 'socrates', 'to', 'make', 'you... what be new , Socrates , to make -PRON- lea... 23 what be new Socrates to make PRON leave P... new Socrates make leave usual haunt Lyceum spe... {'neg': 0.281, 'neu': 0.719, 'pos': 0.0, 'comp...
1 Plato - Complete Works Plato plato Surely you are not prosecuting anyone before t... Surely you are not prosecuting anyone before t... -350 1997 69 surely you are not prosecuting anyone before t... ['surely', 'you', 'are', 'not', 'prosecuting',... surely -PRON- be not prosecute anyone before ... 12 surely PRON be not prosecute anyone before th... surely prosecute anyone king archon {'neg': 0.314, 'neu': 0.349, 'pos': 0.337, 'co...
2 Plato - Complete Works Plato plato The Athenians do not call this a prosecution b... The Athenians do not call this a prosecution b... -350 1997 74 the athenians do not call this a prosecution b... ['the', 'athenians', 'do', 'not', 'call', 'thi... the Athenians do not call this a prosecution ... 11 the Athenians do not call this a prosecution ... Athenians call prosecution indictment Euthyphro {'neg': 0.444, 'neu': 0.556, 'pos': 0.0, 'comp...
3 Plato - Complete Works Plato plato What is this you say? What is this you say? -350 1997 21 what is this you say? ['what', 'is', 'this', 'you', 'say'] what be this -PRON- say ? 5 what be this PRON say say {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...
4 Plato - Complete Works Plato plato Someone must have indicted you, for you are no... Someone must have indicted you, for you are no... -350 1997 101 someone must have indicted you, for you are no... ['someone', 'must', 'have', 'indicted', 'you',... someone must have indict -PRON- , for -PRON- ... 19 someone must have indict PRON for PRON be no... someone must indict go tell indict someone {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...
In [16]:
df = pd.concat([df.drop(['polarity'], axis=1), df['polarity'].apply(pd.Series)], axis=1)
df.head()
Out[16]:
title author school sentence_spacy sentence_str original_publication_date corpus_edition_date sentence_length sentence_lowered tokenized_txt lemmatized_str tokenized_number lemmatized_no_punc lemmatized_clean neg neu pos compound
0 Plato - Complete Works Plato plato What's new, Socrates, to make you leave your ... What's new, Socrates, to make you leave your ... -350 1997 125 what's new, socrates, to make you leave your ... ['what', 'new', 'socrates', 'to', 'make', 'you... what be new , Socrates , to make -PRON- lea... 23 what be new Socrates to make PRON leave P... new Socrates make leave usual haunt Lyceum spe... 0.281 0.719 0.000 -0.4404
1 Plato - Complete Works Plato plato Surely you are not prosecuting anyone before t... Surely you are not prosecuting anyone before t... -350 1997 69 surely you are not prosecuting anyone before t... ['surely', 'you', 'are', 'not', 'prosecuting',... surely -PRON- be not prosecute anyone before ... 12 surely PRON be not prosecute anyone before th... surely prosecute anyone king archon 0.314 0.349 0.337 0.0516
2 Plato - Complete Works Plato plato The Athenians do not call this a prosecution b... The Athenians do not call this a prosecution b... -350 1997 74 the athenians do not call this a prosecution b... ['the', 'athenians', 'do', 'not', 'call', 'thi... the Athenians do not call this a prosecution ... 11 the Athenians do not call this a prosecution ... Athenians call prosecution indictment Euthyphro 0.444 0.556 0.000 -0.4939
3 Plato - Complete Works Plato plato What is this you say? What is this you say? -350 1997 21 what is this you say? ['what', 'is', 'this', 'you', 'say'] what be this -PRON- say ? 5 what be this PRON say say 0.000 1.000 0.000 0.0000
4 Plato - Complete Works Plato plato Someone must have indicted you, for you are no... Someone must have indicted you, for you are no... -350 1997 101 someone must have indicted you, for you are no... ['someone', 'must', 'have', 'indicted', 'you',... someone must have indict -PRON- , for -PRON- ... 19 someone must have indict PRON for PRON be no... someone must indict go tell indict someone 0.000 1.000 0.000 0.0000
In [17]:
def change_to_sentiment(compound):
    if compound > 0:
        return 'Positive'
    elif compound < 0:
        return 'Negative'
    else:
        return 'Neutral'
In [18]:
df['sentiment'] = df['compound'].apply(change_to_sentiment)
In [19]:
df.head()
Out[19]:
title author school sentence_spacy sentence_str original_publication_date corpus_edition_date sentence_length sentence_lowered tokenized_txt lemmatized_str tokenized_number lemmatized_no_punc lemmatized_clean neg neu pos compound sentiment
0 Plato - Complete Works Plato plato What's new, Socrates, to make you leave your ... What's new, Socrates, to make you leave your ... -350 1997 125 what's new, socrates, to make you leave your ... ['what', 'new', 'socrates', 'to', 'make', 'you... what be new , Socrates , to make -PRON- lea... 23 what be new Socrates to make PRON leave P... new Socrates make leave usual haunt Lyceum spe... 0.281 0.719 0.000 -0.4404 Negative
1 Plato - Complete Works Plato plato Surely you are not prosecuting anyone before t... Surely you are not prosecuting anyone before t... -350 1997 69 surely you are not prosecuting anyone before t... ['surely', 'you', 'are', 'not', 'prosecuting',... surely -PRON- be not prosecute anyone before ... 12 surely PRON be not prosecute anyone before th... surely prosecute anyone king archon 0.314 0.349 0.337 0.0516 Positive
2 Plato - Complete Works Plato plato The Athenians do not call this a prosecution b... The Athenians do not call this a prosecution b... -350 1997 74 the athenians do not call this a prosecution b... ['the', 'athenians', 'do', 'not', 'call', 'thi... the Athenians do not call this a prosecution ... 11 the Athenians do not call this a prosecution ... Athenians call prosecution indictment Euthyphro 0.444 0.556 0.000 -0.4939 Negative
3 Plato - Complete Works Plato plato What is this you say? What is this you say? -350 1997 21 what is this you say? ['what', 'is', 'this', 'you', 'say'] what be this -PRON- say ? 5 what be this PRON say say 0.000 1.000 0.000 0.0000 Neutral
4 Plato - Complete Works Plato plato Someone must have indicted you, for you are no... Someone must have indicted you, for you are no... -350 1997 101 someone must have indicted you, for you are no... ['someone', 'must', 'have', 'indicted', 'you',... someone must have indict -PRON- , for -PRON- ... 19 someone must have indict PRON for PRON be no... someone must indict go tell indict someone 0.000 1.000 0.000 0.0000 Neutral
In [20]:
df_sentiment = df.groupby(['school', 'sentiment'])['title'].count().reset_index()
df_sentiment.rename(columns={'title':'count'}, inplace=True)
sns.barplot(data=df_sentiment, x="school", y="count", hue="sentiment")
plt.xticks(rotation=90)
Out[20]:
(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
 [Text(0, 0, 'analytic'),
  Text(1, 0, 'aristotle'),
  Text(2, 0, 'capitalism'),
  Text(3, 0, 'communism'),
  Text(4, 0, 'continental'),
  Text(5, 0, 'empiricism'),
  Text(6, 0, 'feminism'),
  Text(7, 0, 'german_idealism'),
  Text(8, 0, 'nietzsche'),
  Text(9, 0, 'phenomenology'),
  Text(10, 0, 'plato'),
  Text(11, 0, 'rationalism'),
  Text(12, 0, 'stoicism')])
In [21]:
school_total = df.groupby('school')['title'].count().to_dict()
df_sentiment.head()
Out[21]:
school sentiment count
0 analytic Negative 9941
1 analytic Neutral 24427
2 analytic Positive 21057
3 aristotle Negative 9598
4 aristotle Neutral 18254
In [22]:
# create a column of ratio for each sentiment
df_sentiment['total'] = df_sentiment['school'].map(school_total)
df_sentiment['ratio'] = df_sentiment['count'] / df_sentiment['total']
df_sentiment.head()
Out[22]:
school sentiment count total ratio
0 analytic Negative 9941 55425 0.179359
1 analytic Neutral 24427 55425 0.440722
2 analytic Positive 21057 55425 0.379919
3 aristotle Negative 9598 48779 0.196765
4 aristotle Neutral 18254 48779 0.374218
In [23]:
sns.barplot(data=df_sentiment, x="school", y="ratio", hue="sentiment")
plt.xticks(rotation=90)
Out[23]:
(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
 [Text(0, 0, 'analytic'),
  Text(1, 0, 'aristotle'),
  Text(2, 0, 'capitalism'),
  Text(3, 0, 'communism'),
  Text(4, 0, 'continental'),
  Text(5, 0, 'empiricism'),
  Text(6, 0, 'feminism'),
  Text(7, 0, 'german_idealism'),
  Text(8, 0, 'nietzsche'),
  Text(9, 0, 'phenomenology'),
  Text(10, 0, 'plato'),
  Text(11, 0, 'rationalism'),
  Text(12, 0, 'stoicism')])
In [24]:
df_sentiment[df_sentiment['sentiment'] == 'Positive'].sort_values(by = ['ratio'], ascending = False)
Out[24]:
school sentiment count total ratio
8 capitalism Positive 11092 18194 0.609652
35 rationalism Positive 12604 22949 0.549218
17 empiricism Positive 10880 19931 0.545883
32 plato Positive 18899 38366 0.492598
20 feminism Positive 9041 18635 0.485162
23 german_idealism Positive 20093 42136 0.476861
38 stoicism Positive 1174 2535 0.463116
26 nietzsche Positive 6081 13548 0.448849
11 communism Positive 7956 17958 0.443034
5 aristotle Positive 20927 48779 0.429017
29 phenomenology Positive 11169 28573 0.390894
2 analytic Positive 21057 55425 0.379919
14 continental Positive 12300 33779 0.364132
In [25]:
df_sentiment[df_sentiment['sentiment'] == 'Negative'].sort_values(by = ['ratio'], ascending = False)
Out[25]:
school sentiment count total ratio
12 continental Negative 10532 33779 0.311791
18 feminism Negative 5704 18635 0.306091
24 nietzsche Negative 3921 13548 0.289415
36 stoicism Negative 636 2535 0.250888
33 rationalism Negative 4752 22949 0.207068
9 communism Negative 3646 17958 0.203029
3 aristotle Negative 9598 48779 0.196765
15 empiricism Negative 3888 19931 0.195073
30 plato Negative 7359 38366 0.191810
0 analytic Negative 9941 55425 0.179359
6 capitalism Negative 3118 18194 0.171375
27 phenomenology Negative 4722 28573 0.165261
21 german_idealism Negative 6887 42136 0.163447
In [26]:
df_sentiment[df_sentiment['sentiment'] == 'Neutral'].sort_values(by = ['ratio'], ascending = False)
Out[26]:
school sentiment count total ratio
28 phenomenology Neutral 12682 28573 0.443846
1 analytic Neutral 24427 55425 0.440722
4 aristotle Neutral 18254 48779 0.374218
22 german_idealism Neutral 15156 42136 0.359692
10 communism Neutral 6356 17958 0.353937
13 continental Neutral 10947 33779 0.324077
31 plato Neutral 12108 38366 0.315592
37 stoicism Neutral 725 2535 0.285996
25 nietzsche Neutral 3546 13548 0.261736
16 empiricism Neutral 5163 19931 0.259044
34 rationalism Neutral 5593 22949 0.243714
7 capitalism Neutral 3984 18194 0.218973
19 feminism Neutral 3890 18635 0.208747

Conclusion¶

After graphing and comparing the ratio of each sentiment in each shcools, we finally found that capitalism has the most positive texts, continental has the most negative texts, and phenomenology has the most neutral texts. During the process of exploring the data and solving the problem, there are some different aproaches I could use, such as bag of word, nltk, and modeling. The reason I am using the sentiment anlyzer is because this is a new method that I have never used before. I think the result turns out very good and I would like to learn more method of solving problems in the future.